Optimal Integrated Schedule of Entire Process of Dual-blade Multi-cluster Tools From Start-up to Close-down

Multi-cluster tools are widely used in majority of wafer fabrication processes in semiconductor industry. Smaller lot production, thinner circuit width in wafers, larger wafer size, and maintenance have resulted in a large quantity of their start-up and close-down transient periods. Yet, most of existing efforts have been concentrated on scheduling their steady states. Different from such efforts, this work schedules their transient and steady-state periods subject to wafer residency constraints. It gives the schedulability conditions for the steady-state scheduling of dual-blade robotic multi-cluster tools and a corresponding algorithm for finding an optimal schedule. Based on the robot synchronization conditions, a linear program is proposed to figure out an optimal schedule for a start-up period, which ensures a tool to enter the desired optimal steady state. Another linear program is proposed to find an optimal schedule for a close-down period that evolves from the steady state period. Finally, industrial cases are presented to illustrate how the provided method outperforms the existing approach in terms of system throughput improvement.      

基于时间约束集的集束型设备群调度方法

随着300mm晶圆的加工技术问世,工业界开始采用一种全新的晶圆制造设备——集束型设备群(Multi-cluster tools).对于单个集束型设备(Single-cluster tools)调度研究已比较成熟,并提出了多种调度方法,然而对于集束型设备群调度研究尚处在一个起步阶段. 本文对带有驻留约束且具有多种晶圆类型的集束型设备群的调度问题进行了研究,在引入时间约束集概念的基础上建立了调度模型, 同时,提出了一种逐级回溯的调度方法,并对调度算法进行了仿真实验分析. 仿真结果表明本文提出的算法是有效且可行的.     

Global cyclic scheduling: A method to guarantee the timing behavior of distributed real-time systems

The use of the global cyclic scheduling discipline in distributed real-time systems guarantees that the response time requirements of the environment are always met. The global scheduling discipline uses cyclic scheduling plans for its decisions. These plans are determined off-line. During the development phase of a real-time system deadlines are checked and the scheduling plans are generated. Software tools support the computation of the scheduling plans. In this paper we discuss the global cyclic scheduling discipline and present a software system to support the development of distributed real-time systems. Our software system consists of a specificaton tool for the real-time software and a schedule computation tool. Three different algorithms for the computation of the scheduling plans are presented.The research work was supported as an Ernst-von-Siemens scholarship of the Siemens AG.     

Hybrid discrete differential evolution algorithm for biobjective cyclic hoist scheduling with reentrance

Cyclic hoist scheduling problems in automated electroplating lines and surface processing shops attract many attentions and interests both from practitioners and researchers. In such systems, parts are transported from a workstation to another by a material handling hoist. The existing literature mainly addressed how to find an optimal cyclic schedule to minimize the cycle time that measures the productivity of the lines. The material handling cost is an important factor that needs to be considered in practice but seldom addressed in the literature. This study focuses on a biobjective cyclic hoist scheduling problem to minimize the cycle time and the material handling cost simultaneously. We consider the reentrant workstations that are usually encountered in real-life lines but inevitably make the part-flow more complicated. The problem is formulated as a biobjective linear programming model with a given hoist move sequence and transformed into finding a set of Pareto optimal hoist move sequences with respect to the bicriteria. To obtain the Pareto optimal or near-optimal front, a hybrid discrete differential evolution (DDE) algorithm is proposed. In this hybrid evolutional algorithm, the population is divided into several subpopulations according to the maximal work-in-process (WIP) level of the system and the sizes of subpopulations are dynamically adjusted to balance the exploration and exploitation of the search. We propose a constructive heuristic to generate initial subpopulations with different WIP levels, hybrid mutation and crossover operators, an evaluation method that can tackle infeasible individuals and a one-to-one greedy tabu selection method. Computational results on both benchmark instances and randomly generated instances show that our proposed hybrid DDE algorithm outperforms the basic DDE algorithm and can solve larger-size instances than the existing ε-constraint method.     

多集群并行作业的性能监测及分析

介绍一种多集群计算模式,在分析了多集群系统结构灵活、具有可重组性等特点的基础上,研究适用于该模式的并行作业性能监测分析方法与技术,设计和实现了一个并行作业性能监测分析工具。它采用动态性能分析方法,遵循分布式软件设计架构,具有高内聚、低耦合的模块组织结构,运行验证表明其能够在多集群计算模式下有效工作。     

Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation

Digital twin (DT) and artificial intelligence (AI) technologies are powerful enablers for Industry 4.0 toward sustainable resilient manufacturing. Digital twins of machine tools and machining processes combine advanced digital techniques and production domain knowledge, facilitate the enhancement of agility, traceability, and resilience of production systems, and help machine tool builders achieve a paradigm shift from one-time products provision to on-going service delivery. However, the adaptability and accuracy of digital twins at the shopfloor level are restricted by heterogeneous data sources, modeling precision as well as uncertainties from dynamical industrial environments. This article proposes a novel modeling framework to address these inadequacies by in-depth integrating AI techniques and machine tool expertise using aggregated data along the product development process. A data processing procedure is constructed to contextualize metadata sources from the design, planning, manufacturing, and quality stages and link them into a digital thread. On this consistent data basis, a modeling pipeline is presented to incorporate production and machine tool prior knowledge into AI development pipeline, while considering the multi-fidelity nature of data sources in dynamic industrial circumstances. In terms of implementation, we first introduce our existing work for building digital twins of machine tool and manufacturing process. Within this infrastructure, we developed a hybrid learning-based digital twin for manufacturing process following proposed modeling framework and tested it in an external industrial project exemplarily for real-time workpiece quality monitoring. The result indicates that the proposed hybrid learning-based digital twin enables learning uncertainties of the interaction of machine tools and machining processes in real industrial environments, thus allows estimating and enhancing the modeling reliability, depending on the data quality and accessibility. Prospectively, it also contributes to the reparametrization of model parameters and to the adaptive process control.     

Scheduling cluster tools in wafer fabrication using candidate list and simulated annealing

This paper presents a new method for scheduling cluster tools in semiconductor fabrication. A cluster tool consists of a group of single-wafer chambers organized around a wafer transport device, or robot. Cluster fabrication system considered in this paper consists of serial cluster tools. Due to constraints imposed by multiple routes of each wafer type and machines with no buffer, it is difficult to find an optimal or near-optimal schedule. In order to determine the sequence of the operations to be released and the assignment of the machine to each operation, the proposed method uses a job requirement table with random keys as a solution representation. Simulated annealing seeks the optimal or near-optimal sequence and machine assignment of the operations. In this paper, the scheduling objective is to find a schedule with minimum makespan. A Gantt chart is obtained as the final schedule. To handle the constraints, the proposed method uses a candidate list. To determine which operation can be scheduled in considering the constraints, a negotiation procedure between the operations in the candidate list and a current state of the system is introduced. To show the effectiveness of the proposed method, scheduling example of a real cluster fabrication system is presented. Scheduling results are compared with those obtained by using several dispatching rules. From the experimental results, it is shown that the proposed method is promising.     

Scheduling online mixed-parallel workflows of rigid tasks in heterogeneous multi-cluster environments

Workflow scheduling on parallel systems has long been known to be a NP-complete problem. As modern grid and cloud computing platforms emerge, it becomes indispensable to schedule mixed-parallel workflows in an online manner in a speed-heterogeneous multi-cluster environment. However, most existing scheduling algorithms were not developed for online mixed-parallel workflows of rigid data-parallel tasks and multi-cluster environments, therefore they cannot handle the problem efficiently. In this paper, we propose a scheduling framework, named Mixed-Parallel Online Workflow Scheduling (MOWS), which divides the entire scheduling process into four phases: task prioritizing, waiting queue scheduling, task rearrangement, and task allocation. Based on this framework, we developed four new methods: shortest-workflow-first, priority-based backfilling, preemptive task execution and All-EFT task allocation, for scheduling online mixed-parallel workflows of rigid tasks in speed-heterogeneous multi-cluster environments. To evaluate the proposed scheduling methods, we conducted a series of simulation studies and made comparisons with previously proposed approaches in the literature. The experimental results indicate that each of the four proposed methods outperforms existing approaches significantly and all these approaches in MOWS together can achieve more than 20% performance improvement in terms of average turnaround time.     

晶圆重入加工的组合设备终止暂态的调度与分析

晶圆重入是半导体组合设备加工中典型的复杂加工工艺,分析和优化暂态加工过程对于晶圆重入加工具有重要意义.为了满足加工需求和提高组合设备的加工性能,基于稳态重入加工的双臂组合设备Petri网模型和1-晶圆周期调度策略,采用虚拟晶圆的加工模式分析了系统的终止暂态过程,讨论了系统终止暂态的加工时间分布,并给出相应的解析式进行描述.利用eM-Plant仿真平台建立了重入加工的双臂组合设备终止暂态的仿真模型,并用例子验证了1-晶圆周期调度的可行性及解析式的有效性,为研究晶圆重入加工过程的优化提供了有效方法和手段.     

Integration of process planning and scheduling using chaotic particle swarm optimization algorithm

Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi-objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.     

A hybrid cuckoo search-genetic algorithm for hole-making sequence optimization

Biologically-inspired algorithms are stochastic search methods that emulate the behavior of natural biological evolution to produce better solutions and have been widely used to solve engineering optimization problems. In this paper, a new hybrid algorithm is proposed based on the breeding behavior of cuckoos and evolutionary strategies of genetic algorithm by combining the advantages of genetic algorithm into the cuckoo search algorithm. The proposed hybrid cuckoo search-genetic algorithm (CSGA) is used for the optimization of hole-making operations in which a hole may require various tools to machine its final size. The main objective considered here is to minimize the total non-cutting time of the machining process, including the tool positioning time and the tool switching time. The performance of CSGA is verified through solving a set of benchmark problems taken from the literature. The amount of improvement obtained for different problem sizes are reported and compared with those by ant colony optimization, particle swarm optimization, immune based algorithm and cuckoo search algorithm. The results of the tests show that CSGA is superior to the compared algorithms.     

Tool-provisioning strategies for flexible manufacturing systems

The difficulties encountered in managing the tool flow in flexible manufacturing systems for the manufacture of prismatic parts indicate the requirement to handle operational issues such as tool assignment. The choice of operational strategy and its relationship to machine specification, work and tool loading, scheduling and specific mode of tool management may significantly enhance machine utilization and work throughput. In this paper, strategies are presented aimed at improving system efficiency and minimizing tooling costs by considering tool provision as work-orientated, where the tooling is changed to suit the production task, or tool-orientated, where resident tools dictate the work flow, or a combination of the two. This paper draws on evidence from current industrial practice and recent developments.     

Energy-efficient power/rate control and scheduling in hybrid TDMA/CDMA wireless sensor networks

We consider a hybrid TDMA/CDMA wireless sensor network (WSN) and quantitatively investigate the energy efficiency obtained by combining adaptive power/rate control with time-domain scheduling. The energy efficiency improvement is carried out with respect to interfering-cluster scheduling, intra-cluster node scheduling, and transmission powers and times (durations) control (PTC) for individual nodes. The interfering-cluster scheduling is formulated as a vertex-coloring problem, which can be solved efficiently using existing numerical algorithms in graph theory. For the node scheduling problem, we present a heuristic algorithm, which iteratively searches for the best schedule in such a way that the energy consumption keeps decreasing after every iteration. Compared with the exponentially complicated exhaustive search algorithm, this heuristic algorithm has polynomial computing complexity and can provide optimal solutions in the most simulated cases. For the transmission power/time control, two simplified PTC schemes, namely, PTC-UT and PTC-USG, are proposed and studied based on our previous optimization work PTC-IPT. We show that PTC-UT and PTC-USG provide comparable energy efficiency to PTC-IPT at only half of its complexity. Numerical examples are used to validate our findings.     

RCPSP中现金流优化问题的HGA方法

针对网络进度计划中财务方面对项目管理的影响 ,研究资源受限项目调度问题 (RCPSP)中网络现金流的优化问题。提出以网络净现值最大作为网络现金流优化的目标 ,建立了带有贴现率的非线性整数规划模型 ,采用遗传算法与模拟退火算法相结合的混合式遗传算法进行求解。仿真实例表明了方法的合理性和有效性。     

Design of hybrid differential evolution and group method of data handling networks for modeling and prediction

This paper proposes a hybrid modeling approach based on two familiar non-linear methods of mathematical modeling; the group method of data handling (GMDH) and differential evolution (DE) population-based algorithm. The proposed method constructs a GMDH self-organizing network model of a population of promising DE solutions. The new hybrid implementation is then applied to modeling tool wear in milling operations and also applied to two representative time series prediction problems of exchange rates of three international currencies and the well-studied Box-Jenkins gas furnace process data. The results of the proposed DE–GMDH approach are compared with the results obtained by the standard GMDH algorithm and its variants. Results presented show that the proposed DE–GMDH algorithm appears to perform better than the standard GMDH algorithm and the polynomial neural network (PNN) model for the tool wear problem. For the exchange rate problem, the results of the proposed DE–GMDH algorithm are competitive with all other approaches except in one case. For the Box-Jenkins gas furnace data, the experimental results clearly demonstrates that the proposed DE–GMDH-type network outperforms the existing models both in terms of better approximation capabilities as well as generalization abilities. Consequently, this self-organizing modeling approach may be useful in modeling advanced manufacturing systems where it is necessary to model tool wear during machining operations, and in time series applications such as in prediction of time series exchange rate and industrial gas furnace problems.     

Estimation of NC machining time using NC block distribution for sculptured surface machining

The estimation of NC machining time is of importance because it provides manufacturing engineers with information to accurately predict the productivity of an NC machine, as well as its production schedule. NC programs contain various machining information, such as tool positions, feed and speed rates, and other machine instructions. Nominal NC machining time can easily be obtained based on the NC program data. Actual machining time, however, cannot simply be found due to the dynamic characteristics of a NC machine controller, such as acceleration and deceleration effect. Hence, this study presents an NC machine time estimation model for machining sculptured surfaces, considering such dynamic characteristics of the machine. The proposed estimation model uses several factors, such as the distribution of NC blocks, angle between the blocks, federates, acceleration and deceleration constants, classifying tool feed rate patterns into four types based on the acceleration and deceleration profile, NC block length, and minimum feed rate. However, there exists an error for the actual machining time due to the lack of the measurement equipment or tools to gauge an exact minimum feed rate. Thus, this paper proposes a machining time estimation model using NC block distributions, lowering down the error caused by the inaccurate minimum feed rate. The proposed machining time estimator performs at around 10% of mean error.     

Steady-State Throughput and Scheduling Analysis of Multicluster Tools: A Decomposition Approach

Cluster tools are widely used as semiconductor manufacturing equipment. While throughput analysis and scheduling of single-cluster tools have been well-studied, research work on multicluster tools is still at an early stage. In this paper, we analyze steady-state throughput and scheduling of multicluster tools. We consider the case where all wafers follow the same visit flow within a multicluster tool. We propose a decomposition method that reduces a multicluster tool problem to multiple independent single-cluster tool problems. We then apply the existing and extended results of throughput and scheduling analysis for each single-cluster tool. Computation of lower-bound cycle time (fundamental period) is presented. Optimality conditions and robot schedules that realize such lower-bound values are then provided using ldquopullrdquo and ldquoswaprdquo strategies for single-blade and double-blade robots, respectively. For an -cluster tool, we present lower-bound cycle time computation and robot scheduling algorithms. The impact of buffer/process modules on throughput and robot schedules is also studied. A chemical vapor deposition tool is used as an example of multicluster tools to illustrate the decomposition method and algorithms. The numerical and experimental results demonstrate that the proposed decomposition approach provides a powerful method to analyze the throughput and robot schedules of multicluster tools.     

Solving single machine scheduling under disruption with discounted costs by quantum-inspired hybrid heuristics

In this paper we study machine disruption on scheduling problem. We focus on the case where the weighted discounted shortest processing time (WDSPT) rule is optimal for original single machine scheduling problem. After a subset of jobs have finished processing, we learn that the machine would be disrupted for some period of time in the future. Therefore a new schedule is needed considering both original objective and the deviation from the initial schedule. The original objective is measured by the weighted discounted total completion time and the deviation is measured by the variances in jobs’ completion times. According to the characteristics of optimal schedule, we design one hybrid heuristic algorithm, combining the advantages of qubit representation in quantum computing and Non-dominated Sorting Genetic Algorithm (NSGA-II). By analyzing the solutions diversity and proximity to optimal Pareto front on several metrics, we demonstrate that the proposed algorithm is effective for machine disruption management.     

Human-robot collaboration while sharing production activities in dynamic environment: SPADER system

Interactive robot doing collaborative work in hybrid work cell need adaptive trajectory planning strategy. Indeed, systems must be able to generate their own trajectories without colliding with dynamic obstacles like humans and assembly components moving inside the robot workspace. The aim of this paper is to improve collision-free motion planning in dynamic environment in order to insure human safety during collaborative tasks such as sharing production activities between human and robot. Our system proposes a trajectory generating method for an industrial manipulator in a shared workspace. A neural network using a supervised learning is applied to create the waypoints required for dynamic obstacles avoidance. These points are linked with a quintic polynomial function for smooth motion which is optimized using least-square to compute an optimal trajectory. Moreover, the evaluation of human motion forms has been taken into consideration in the proposed strategy. According to the results, the proposed approach is an effective solution for trajectories generation in a dynamic environment like a hybrid workspace.     

Multicast protocols for scalable on-demand download

Previous scalable protocols for downloading large, popular files from a single server include batching and cyclic multicast. With batching, clients wait to begin receiving a requested file until the beginning of its next multicast transmission, which collectively serves all of the waiting clients that have accumulated up to that point. With cyclic multicast, the file data is cyclically transmitted on a multicast channel. Clients can begin listening to the channel at an arbitrary point in time, and continue listening until all of the file data has been received.This paper first develops lower bounds on the average and maximum client delay for completely downloading a file, as functions of the average server bandwidth used to serve requests for that file, for systems with homogeneous clients. The results show that neither cyclic multicast nor batching consistently yields performance close to optimal. New hybrid download protocols are proposed that achieve within 15% of the optimal maximum delay and 20% of the optimal average delay in homogeneous systems.For heterogeneous systems in which clients have widely varying achievable reception rates, an additional design question concerns the use of high rate transmissions, which can decrease delay for clients that can receive at such rates, in addition to low rate transmissions that can be received by all clients. A new scalable download protocol for such systems is proposed, and its performance is compared to that of alternative protocols as well as to new lower bounds on maximum client delay. The new protocol achieves within 25% of the optimal maximum client delay in all scenarios considered.